Staggered-peak production is a mixed blessing in the control of particulate matter pollution

Staggered-peak production (SP)—a measure to halt industrial production in the heating season—has been implemented in North China Plain to alleviate air pollution. We compared the variations of PM1 composition in Beijing during the SP period in the 2016 heating season (SPhs) with those in the normal production (NP) periods during the 2015 heating season (NPhs) and 2016 non-heating season (NPnhs) to investigate the effectiveness of SP. The PM1 mass concentration decreased from 70.0 ± 54.4 μg m−3 in NPhs to 53.0 ± 56.4 μg m−3 in SPhs, with prominent reductions in primary emissions. However, the fraction of nitrate during SPhs (20.2%) was roughly twice that during NPhs (12.7%) despite a large decrease of NOx, suggesting an efficient transformation of NOx to nitrate during the SP period. This is consistent with the increase of oxygenated organic aerosol (OOA), which almost doubled from NPhs (22.5%) to SPhs (43.0%) in the total organic aerosol (OA) fraction, highlighting efficient secondary formation during SP. The PM1 loading was similar between SPhs (53.0 ± 56.4 μg m−3) and NPnhs (50.7 ± 49.4 μg m−3), indicating a smaller difference in PM pollution between heating and non-heating seasons after the implementation of the SP measure. In addition, a machine learning technique was used to decouple the impact of meteorology on air pollutants. The deweathered results were comparable with the observed results, indicating that meteorological conditions did not have a large impact on the comparison results. Our study indicates that the SP policy is effective in reducing primary emissions but promotes the formation of secondary species.


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Supplementary Note 1 PMF receptor model was conducted through the interface SoFi coded in Igor Wavemetrics (Source Finder) 1 . PMF is a bilinear receptor model which enables to describe the variability of a multivariate database as the linear combination of static factor profiles and the corresponding time series: where X is the measurement matrix, G contains the factor time series, F is the factor profiles and E is the model residuals. The model uses the least squares approach to iteratively minimize the quantity Q, defined as the sum of the squared residuals (eij) weighted by their respective uncertainties (σij): In recent years, there were lots of studies using the PMF receptor model for the OA source apportionment [2][3][4][5][6][7][8][9][10][11][12][13][14] . Nevertheless, the PMF receptor model usually has difficulties in separating the factors with similar profiles, such as cooking and traffic factors 15 . Therefore, the PMF receptor model was just used to help us to determine the right number of factors in this study and then the multilinear engine (ME-2) which enabled complete exploration of the rotational ambiguity by introducing a priori information as additional model input was used: where f refers to a row of the matrix F. j represents the mass to charge ratio (m/z) of the ions, and a value determines the extent to which the output profiles can differ from the model inputs and it ranges from 0 to 1.
In this work, we examined solutions from 2 to 8 factors by using the unconstrained PMF In order to separate HOA and COA in ME-2, we constrained the HOA and COA profile from Crippa et al. 17 which was derived from Paris. And we used the profile derived from Beijing winter 2014 18 to constrain BBOA. For ME-2 results with constrained HOA, COA and BBOA, one unconstrained factor was always present and was characterized by high signals at m/z 77, m/z 91, m/z 115 and m/z 44, indicating that CCOA was mixed with OOA. To separate CCOA from OOA, we constrained the CCOA profile from Wang et al. 12 which was derived from Baoji. Therefore, we finally constrained four factors (HOA, COA, CCOA, and BBOA) in the ME-2 using an a values between 0 and 1 with a step of 0.1 to re-adjust the input profiles and to minimize the effect of using non-local input profiles. As for all 14641 possible combinations of a values, a set of three criteria were established to select more environmentally meaningful results and to optimize the OA source apportionment. 4 1. Minimization of m/z 60 in HOA. The threshold for maximal fractional contributions of m/z 60 in HOA is 0.006 according to profiles derived from multiple ambient data sets (mean + 2σ) 19 .
2. The consistency of factors with previous studies [12][13][14]20 . For example, profiles of OOA should have notable peaks of m/z 44 and weak signals at high m/z which is related to PAHs.
3. Optimization of COA diurnal patterns. COA has not established clear markers, so it's difficult to use COA profiles to optimize the apportionment of this source, but it's diurnal cycle can be a valuable characteristic for its identification. The COA diurnals have distinct peaks at meal times especially lunch and dinner times. Therefore, a novel approach using k means cluster analysis was utilized to group the normalized COA diurnals of all possible a value combinations. As the basis of the cost function (CF) shown in the Eq. (4), we can minimize the term T1 which represents the sum of the Euclidian distances between each observations (xi) and its respective cluster center (uzi) through increasing the number of clusters (k), but at the same time, the higher values of k will also lead to more complexity to the solution. Therefore, the second term (T2) which can be expressed as the product of the number of clusters (k) and the logarithm of the dimensionality of the cluster (D = 24 h in our case) was introduced to penalize the complexity of the higher order solutions according to Bayesian information criterion.
As shown in Supplementary Figure 3, there were the cluster analysis results for four-, five-, six-cluster solutions. The left plot represented all diurnal patterns belonging to different clusters with different colors, the right plot indicated the diurnals of the cluster center. We can see from 5 Supplementary Figure 3 (a) that a minimum in the cost function was gotten at 5-cluster solution, which was finally chosen to be the optimal cluster number representing different types of COA diurnal patterns. From the five-cluster solution in Supplementary Figure 3  High concentration of LSOA accumulated around the sampling site with no specific direction when the wind speed was less than 2 m s -1 , while the high mass concentration of RSOA appeared at high wind speed (close to 4 m s -1 or slightly greater than 4 m s -1 ) which was mainly from the southeast. These characteristics indicated that the two factors were different SOA 7 sources and RSOA showed regional transmission features while LSOA was mainly from local formation.

Supplementary Figures
Supplementary Figure 1  Note: a dew represents deweathered results; obs represents observed results.